Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142047
Title: Dealing with confounders in omics analysis
Authors: Goh, Wilson Wen Bin
Wong, Limsoon
Keywords: Science::Biological sciences
Issue Date: 2018
Source: Goh, W. W. B., & Wong, L. (2018). Dealing with confounders in omics analysis. Trends in biotechnology, 36(5), 488-498. doi:10.1016/j.tibtech.2018.01.013
Journal: Trends in biotechnology
Abstract: The Anna Karenina effect is a manifestation of the theory-practice gap that exists when theoretical statistics are applied on real-world data. In the course of analyzing biological data for differential features such as genes or proteins, it derives from the situation where the null hypothesis is rejected for extraneous reasons (or confounders), rather than because the alternative hypothesis is relevant to the disease phenotype. The mechanics of applying statistical tests therefore must address and resolve confounders. It is inadequate to simply rely on manipulating the P-value. We discuss three mechanistic elements (hypothesis statement construction, null distribution appropriateness, and test-statistic construction) and suggest how they can be designed to foil the Anna Karenina effect to select phenotypically relevant biological features.
URI: https://hdl.handle.net/10356/142047
ISSN: 0167-7799
DOI: 10.1016/j.tibtech.2018.01.013
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SBS Journal Articles

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